CGM properties in VELA and NIHAO simulations; the OVI ionization mechanism: dependence on redshift, halo mass, and radius
Bibliographic record
Abstract
We study the components of cool and warm/hot gas in the circumgalactic medium (CGM) of simulated galaxies and address the relative production of OVI by photoionization versus collisional ionization, as a function of halo mass, redshift, and distance from the galaxy halo centre. This is done utilizing two different suites of zoom-in hydro-cosmological simulations, VELA (6 haloes; z > 1) and NIHAO (18 haloes; to z = 0), which provide a broad theoretical basis because they use different codes and physical recipes for star formation and feedback. In all haloes studied in this work, we find that collisional ionization by thermal electrons dominates at high redshift, while photoionization of cool or warm gas by the metagalactic radiation takes over near z ∼ 2. In haloes of |${\sim } 10^{12}\, \mathrm{M}_{\odot }$| and above, collisions become important again at z < 0.5, while photoionization remains significant down to z = 0 for less massive haloes. In haloes with |$M_{\textrm{v}}\gt 3\times 10^{11}\, \mathrm{M}_{\odot }$|, at z ∼ 0 most of the photoionized OVI is in a warm, not cool, gas phase (T ≲ 3 × 105 K). We also find that collisions are dominant in the central regions of haloes, while photoionization is more significant at the outskirts, around Rv, even in massive haloes. This too may be explained by the presence of warm gas or, in lower mass haloes, by cool gas inflows.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".